Abraham, S. and Nair, S. (2014) Cyber Security Analytics: A Stochastic Model for Security Quantification Using Absorbing Markov Chains. Journal of Communications, 9, 899-907.
https://doi.org/10.12720/jcm.9.12.899-907

has been cited by the following article:

• TITLE: Cybersecurity: A Stochastic Predictive Model to Determine Overall Network Security Risk Using Markovian Process

  AUTHORS: Nawa Raj Pokhrel, Chris P. Tsokos

  KEYWORDS: Vulnerability, Attack Graph, Markov Model, Exploitability, CVSS, FIRST, NVD, IDS

  JOURNAL NAME: Journal of Information Security, Vol.8 No.2, April 28, 2017

  ABSTRACT: There are several security metrics developed to protect the computer networks. In general, common security metrics focus on qualitative and subjective aspects of networks lacking formal statistical models. In the present study, we propose a stochastic model to quantify the risk associated with the overall network using Markovian process in conjunction with Common Vulnerability Scoring System (CVSS) framework. The model we developed uses host access graph to represent the network environment. Utilizing the developed model, one can filter the large amount of information available by making a priority list of vulnerable nodes existing in the network. Once a priority list is prepared, network administrators can make software patch decisions. Gaining in depth understanding of the risk and priority level of each host helps individuals to implement decisions like deployment of security products and to design network topologies.